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shamolvzhou金虫 (正式写手)
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[求助]
文献检索求助
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请求看看下面这篇文章是否被 SCI, EI和SSCI检索啦, 非常感谢. Zhang, Zhong-Yuan, Kai-Di Sun, and Si-Qi Wang. "Enhanced Community Structure Detection in Complex Networks with Partial Background Information." Scientific reports 3 (2013). |
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至尊木虫 (知名作家)
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shamolvzhou(oven1986代发): 金币+10, 代扣结帖。 2014-04-15 10:24:07
oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-04-15 10:24:17
shamolvzhou(oven1986代发): 金币+10, 代扣结帖。 2014-04-15 10:24:07
oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-04-15 10:24:17
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SCI收录 Enhanced Community Structure Detection in Complex Networks with Partial Background Information 作者:Zhang, ZY (Zhang, Zhong-Yuan)[ 1 ] ; Sun, KD (Sun, Kai-Di)[ 1 ] ; Wang, SQ (Wang, Si-Qi)[ 1 ] SCIENTIFIC REPORTS 卷: 3 文献号: UNSP 3241 DOI: 10.1038/srep03241 出版年: NOV 19 2013 查看期刊信息 摘要 Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework. 作者信息 通讯作者地址: Zhang, ZY (通讯作者) Cent Univ Finance & Econ, Sch Math & Stat, Beijing, Peoples R China. 地址: [ 1 ] Cent Univ Finance & Econ, Sch Math & Stat, Beijing, Peoples R China 电子邮件地址:zhyuanzh@gmail.com 基金资助致谢 基金资助机构 授权号 National Natural Science Foundation of China 61203295 Program for Innovation Research in Central University of Finance and Economics 查看基金资助信息 出版商 NATURE PUBLISHING GROUP, MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 类别 / 分类 研究方向:Science & Technology - Other Topics Web of Science 类别:Multidisciplinary Sciences 文献信息 文献类型:Article 语种:English 入藏号: WOS:000327515300002 ISSN: 2045-2322 其他信息 IDS 号: 259GL Web of Science 核心合集中的 "引用的参考文献": 14 Web of Science 核心合集中的 "被引频次": 0 |
5楼2014-03-02 10:14:03